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Small Area Estimation: a Practitioner’s Appraisal

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  • Dan HEDLIN

Abstract

Demands of regional statistics combined with pressure to reduce costs and response burden have lead to great interest in Small Area Estimation. Both researchers and practitioners take part in a largely very successful development that still is moving rapidly. However, the National Statistical Institutes in Europe have been rather hesitant to implement SAE, partly because of the different tradition of SAE in terms of statistical inference. NSIs are obliged to as far as possible publish officials statistics that are based on estimators with negligible bias. Fear of model misspecification has been a hindrance to wide application of SAE. Use of a model is now seen as a quality issue. Communication of methods and the resulting quality of statistics is an issue that NSIs have recently given specific attention to.

Suggested Citation

  • Dan HEDLIN, 2008. "Small Area Estimation: a Practitioner’s Appraisal," Rivista Internazionale di Scienze Sociali, Vita e Pensiero, Pubblicazioni dell'Universita' Cattolica del Sacro Cuore, vol. 116(4), pages 407-417.
    • Handle: RePEc:vep:journl:y:2008:v:116:i:4:p:407-417
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    Cited by:

    1. Enrico Fabrizi & Maria Ferrante & Carlo Trivisano, 2013. "Small area estimation of labor productivity for the Italian manufacturing SME cross-classified by region, industry and size," ERSA conference papers ersa13p894, European Regional Science Association.

    More about this item

    Keywords

    implicit models; explicit models; official statistics; communication of statistical quality;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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